Author

Ahmed El-Kotory

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Preprint Aug 2026

[DeepGOF] Where Does a Logistic Risk Model Fail? An Audited Neural Goodness-of-Fit Test for Model Development and External Validation

Goodness-of-fit tests for logistic regression are routine in clinical risk modelling, yet the classical tests say whether a model misfits, not where. A pretrained network is not a test until its level, power and blind spots are established. We audit DeepGOF-1, which renders the residuals of a fitted logistic model as a...

Ebrahim Khaled Ebrahim, Osama Abd El-Aziz Hussein, Ahmed El-Kotory · 0 citations

Preprint Aug 2026

[EDGE] A grouped calibration test for logistic regression that tolerates a few corrupted records

EDGE, the test proposed here, projects the grouped residuals onto a small basis of calibration shapes that do not depend on the number of groups, so the partition becomes a setting, fine-grained for power and coarse when records may be wrong, and its reference needs no resampling.

Ebrahim Khaled Ebrahim, Osama Abd El-Aziz Hussein, Ahmed El-Kotory · 1 citation

Preprint Sep 2026

Three-group variance-ratio tests for heteroscedasticity in linear regression: an exact null distribution and an outlier-resistant version

Tests for heteroscedasticity in linear regression lose their level or power in three situations: when the data contain outliers, when the variance is not monotone, and when it changes along a variable outside the regressors, such as time. The proposed tests sort the observations by any chosen variable, split them into...

Ahmed El-Kotory, Ebrahim Khaled Ebrahim · 0 citations

Preprint Aug 2026

EDGE: a closed-form directed test for the calibration of probabilistic binary classifiers

A probabilistic binary classifier is judged almost everywhere by discrimination - accuracy, the ROC curve, the area under it. Every such criterion is invariant to a monotone distortion of the predicted probabilities, so a classifier can rank perfectly and still return probabilities that are badly wrong. Calibration is...

Ebrahim Khaled Ebrahim, Ahmed El-Kotory · 1 citation

Preprint Jul 2026

A directional Hosmer-Lemeshow goodness-of-fit test for sparse logistic regression

Goodness-of-fit assessment for the binary logistic regression model is difficult when covariates are continuous: the data are effectively sparse, the classical Pearson and deviance tests fail, and practitioners rely on partition-based tests, such as the Hosmer-Lemeshow test, that group observations before comparing obs...

Ebrahim Khaled Ebrahim, Ahmed El-Kotory · 1 citation

Preprint Jul 2026

Benchmarking Goodness-of-Fit and Calibration Algorithms for Logistic Regression Classifiers: A Large-Scale Simulation Study under Sparse Data

This paper provides a unified taxonomy and a large-scale, reproducible simulation benchmark; more than twenty tests are implemented in the open-source R package ebrahim.gof, and translate these findings into practical, evidence-based guidance for assessing logistic regression fit.

Ebrahim Khaled Ebrahim, Ahmed El-Kotory · 0 citations